skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Search for: All records

Creators/Authors contains: "Chen, Yudong"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available January 31, 2026
  2. Free, publicly-accessible full text available January 31, 2026
  3. Free, publicly-accessible full text available January 30, 2026
  4. Free, publicly-accessible full text available January 22, 2026
  5. Free, publicly-accessible full text available October 10, 2025
  6. Free, publicly-accessible full text available September 25, 2025
  7. Free, publicly-accessible full text available September 25, 2025
  8. Free, publicly-accessible full text available September 25, 2025
  9. In this work, we investigate stochastic approximation (SA) with Markovian data and nonlinear updates under constant stepsize. Existing work has primarily focused on either i.i.d. data or linear update rules. We take a new perspective and carefully examine the simultaneous presence of Markovian dependency of data and nonlinear update rules, delineating how the interplay between these two structures leads to complications that are not captured by prior techniques. By leveraging the smoothness and recurrence properties of the SA updates, we develop a fine-grained analysis of the correlation between the SA iterates and Markovian data. This enables us to overcome the obstacles in existing analysis and establish for the first time the weak convergence of the joint process. Furthermore, we present a precise characterization of the asymptotic bias of the SA iterates. As a by-product of our analysis, we derive finite-time bounds on higher moment and present non-asymptotic geometric convergence rates for the iterates, along with a Central Limit Theorem. 
    more » « less
    Free, publicly-accessible full text available September 25, 2025
  10. Free, publicly-accessible full text available June 30, 2025